Distributed computation of fast consensus weights using ADMM

In time-critical multi-agent tasks, it is important for the agents to reach consensus as fast possible. this paper, we consider problem of computing weights in weighted-average protocol that achieve average with an optimal per-step convergence factor. Most work literature either computes these set a centralized manner, which requires global information about network may not be available, or suboptimal weights, are slow achieving consensus. We propose iterative, distributed algorithm compute give theoretical guarantees algorithm. Through numerical examples, show our method performs better than other methods consensus, and matches performance method.